Evidence map›Paper›PMID 42011418›Full record

ArticleNeuroimage. Reports2026

Estimating white matter hyperintensities volume in individuals with stroke using T1-weighted images.

Mahir H Khan, Stuti Chakraborty, Jennifer K Ferris, Lara A Boyd, Mohamed Salah Khlif, Amy Brodtmann, Michael R Borich, James H Cole, Steven C Cramer, Niko H Fullmer and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroimage. Reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Mahir H KhanNeuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
Stuti ChakrabortyChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Jennifer K FerrisGerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada.
Lara A BoydDepartment of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Mohamed Salah KhlifDepartment of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Amy BrodtmannDepartment of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Michael R BorichDepartment of Rehabilitation Medicine, Emory University, Atlanta, GA, USA.
James H ColeHawkes Institute, Department of Computer Science, University College London, London, UK.
Steven C CramerDepartment of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
Niko H FullmerResearch Institute, Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA.
Jeanette R GumarangResearch Institute, Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA.
Hosung KimUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Amisha KumarDornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA.
Octavio Marin-PardoChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Susan M MurphyDepartment of Rehabilitation Medicine, Emory University, Atlanta, GA, USA.
Emily R RosarioResearch Institute, Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA.
Heidi M SchambraDepartment of Neurology, New York University Langone Health, New York, NY, USA.
Grace C SongChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Sook-Lei LiewNeuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.

Funding

Data Science and Analytics for Precision Rehabilitation (DAPR) Center - Resource CoreP50HD118603 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Sook-Lei Liew · 2025 to 2026
$4.0M
Supplement to Effects of global brain health on sensorimotor recovery after strokeR01NS115845 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LIEW, SOOK-LEI · 2020 to 2024
$3.2M
Global Brain Health Predictors of Post-Stroke Sensorimotor Recovery using AI-Enhanced Clinical MRIsRF1NS115845 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LIEW, SOOK-LEI · 2025 to 2025
$2.9M
NICHD NIH HHS P50 HD118603NINDS NIH HHS R01 NS115845NINDS NIH HHS RF1 NS115845
6 · The paper itself

Abstract

Stroke recovery outcomes vary across individuals, motivating the search for biomarkers that can improve prediction. White matter hyperintensities (WMH) volume is a leading biomarker candidate, with FLAIR MRI typically used for WMH segmentation; however, T1-weighted (T1) MRI is often more available. Therefore, we evaluated the performance of two automated WMH segmentation methods (WMH-SynthSeg, SAMSEG) to determine whether WMH volume can be reliably estimated using T1 alone. We analyzed imaging data from 227 stroke patients across three datasets spanning early subacute to chronic recovery, with gold-standard WMH masks and stroke lesion masks manually traced on available T1 and FLAIR scans. WMH was segmented using T1 only as input to WMH-SynthSeg and SAMSEG, as well as using both T1 and FLAIR as input to SAMSEG, as previously implemented in stroke recovery research. Automated WMH segmentations were compared to the gold-standard WMH mask: spatial accuracy was assessed using Dice similarity index (SI) and cluster-level false negative ratio, while volumetric agreement was assessed using intraclass correlation, Pearson's correlation, and volume ratio. We used linear mixed-effects models to evaluate whether SI was influenced by factors such as WMH volume, stroke lesion volume, WMH contrast, age, sex, and days since stroke, with dataset as a random effect. WMH-SynthSeg using T1-only input produced more accurate and reliable WMH segmentations compared to SAMSEG with T1-only input and performed comparably to SAMSEG using both T1 and FLAIR input. WMH-SynthSeg using T1-only input may be used for WMH volume estimation in stroke recovery research in the absence of multimodal imaging.

Indexed as

Automated segmentationNeuroimaging biomarkersStroke recoveryWhite matter hyperintensities

Identifiers

PMID42011418
PMCPMC13092073

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.